Support llama3 eagle3 head with llama4 verifier (#25961)

Signed-off-by: rahul-tuli <rtuli@redhat.com>
Signed-off-by: Rahul Tuli <rtuli@redhat.com>

vllm/model_executor/models/llama.py

@@ -604,6 +604,11 @@ class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsEagle3):
         self.model.aux_hidden_state_layers = layers
 
     def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]:
+        """Override to return default layers for Llama
+
+        Note: The GPU model runner will override this with layers from
+        the speculative config if available, providing dynamic configuration.
+        """
         num_layers = len(self.model.layers)
         return (2, num_layers // 2, num_layers - 3)
 

vllm/model_executor/models/llama_eagle3.py

@@ -21,6 +21,7 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
 )
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.llama import LlamaDecoderLayer, LlamaForCausalLM
+from vllm.multimodal.inputs import NestedTensors
 
 from .utils import AutoWeightsLoader, maybe_prefix
 
@@ -241,7 +242,12 @@ class Eagle3LlamaForCausalLM(LlamaForCausalLM):
             requires_grad=False,
         )
 
-    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[NestedTensors] = None,
+        is_multimodal: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
         return self.model.get_input_embeddings(input_ids)
 
     def forward(

vllm/model_executor/models/mllama4.py

@@ -64,7 +64,12 @@ from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
-from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .interfaces import (
+    MultiModalEmbeddings,
+    SupportsEagle3,
+    SupportsMultiModal,
+    SupportsPP,
+)
 from .llama4 import Llama4ForCausalLM
 from .utils import AutoWeightsLoader, flatten_bn, maybe_prefix
 from .vision import run_dp_sharded_vision_model
@@ -717,7 +722,9 @@ class Mllama4DummyInputsBuilder(BaseDummyInputsBuilder[Mllama4ProcessingInfo]):
     info=Mllama4ProcessingInfo,
     dummy_inputs=Mllama4DummyInputsBuilder,
 )
-class Llama4ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
+class Llama4ForConditionalGeneration(
+    nn.Module, SupportsMultiModal, SupportsPP, SupportsEagle3
+):
     packed_modules_mapping = {
         "qkv_proj": ["q_proj", "k_proj", "v_proj"],
         "gate_up_proj": ["gate_proj", "up_proj"],
@@ -767,6 +774,22 @@ class Llama4ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
             self.language_model.make_empty_intermediate_tensors
         )
 
+    def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None:
+        """Set which layers should output auxiliary hidden states for EAGLE3."""
+        # Delegate to underlying language model (Llama4ForCausalLM)
+        assert hasattr(self.language_model, "set_aux_hidden_state_layers")
+        self.language_model.set_aux_hidden_state_layers(layers)
+
+    def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]:
+        """Get the layer indices for auxiliary hidden state outputs.
+
+        Note: The GPU model runner will override this with layers from
+        the speculative config if available, providing dynamic configuration.
+        """
+        # Delegate to underlying language model (Llama4ForCausalLM)
+        assert hasattr(self.language_model, "get_eagle3_aux_hidden_state_layers")
+        return self.language_model.get_eagle3_aux_hidden_state_layers()
+
     def _parse_and_validate_image_input(
         self, **kwargs: object
     ) -> Optional[Llama4ImagePatchInputs]:

vllm/transformers_utils/configs/speculators/algos.py

@@ -21,6 +21,10 @@ def update_eagle3(config_dict: dict, vllm_config: dict) -> None:
     - draft_vocab_size: Size of the draft model's vocabulary
     - target_hidden_size: Hidden size of the target model
     - norm_before_residual: Whether to apply norm before residual connection
+    - eagle_aux_hidden_state_layer_ids: List of layer indices from the base
+        model to use as auxiliary inputs for the Eagle3 drafter. These layers
+        provide intermediate hidden states that help the drafter make better
+        predictions. This is the standard field used in Eagle3 checkpoints.
     """
 
     vllm_config["draft_vocab_size"] = config_dict.get("draft_vocab_size")
@@ -28,3 +32,7 @@ def update_eagle3(config_dict: dict, vllm_config: dict) -> None:
         vllm_config["target_hidden_size"] = config_dict["target_hidden_size"]
     vllm_config["norm_before_residual"] = config_dict.get("norm_before_residual", True)
     vllm_config["architectures"] = ["Eagle3LlamaForCausalLM"]
+    if config_dict.get("eagle_aux_hidden_state_layer_ids"):
+        vllm_config["eagle_aux_hidden_state_layer_ids"] = config_dict[
+            "eagle_aux_hidden_state_layer_ids"
+        ]

vllm/v1/worker/gpu_model_runner.py

@@ -2943,15 +2943,24 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                 logger.info("Loading drafter model...")
                 self.drafter.load_model(self.model)
             if self.use_aux_hidden_state_outputs:
-                if supports_eagle3(self.model):
-                    self.model.set_aux_hidden_state_layers(
-                        self.model.get_eagle3_aux_hidden_state_layers()
-                    )
-                else:
+                if not supports_eagle3(self.model):
                     raise RuntimeError(
                         "Model does not support EAGLE3 interface but "
                         "aux_hidden_state_outputs was requested"
                     )
+
+                # Try to get auxiliary layers from speculative config,
+                # otherwise use model's default layers
+                aux_layers = self._get_eagle3_aux_layers_from_config()
+                if aux_layers:
+                    logger.info(
+                        "Using auxiliary layers from speculative config: %s",
+                        aux_layers,
+                    )
+                else:
+                    aux_layers = self.model.get_eagle3_aux_hidden_state_layers()
+
+                self.model.set_aux_hidden_state_layers(aux_layers)
             time_after_load = time.perf_counter()
         self.model_memory_usage = m.consumed_memory
         logger.info(
@@ -3006,6 +3015,30 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                     self.model, self.vllm_config, CUDAGraphMode.NONE, self.device
                 )
 
+    def _get_eagle3_aux_layers_from_config(self) -> Optional[tuple[int, ...]]:
+        """Extract Eagle3 auxiliary layer indices from speculative config.
+
+        These indices specify which hidden states from the base model should
+        be used as auxiliary inputs for the Eagle3 drafter model during
+        speculative decoding.
+
+        Returns:
+            Tuple of layer indices if found in draft model config,
+            None otherwise.
+        """
+        if not (self.speculative_config and self.speculative_config.draft_model_config):
+            return None
+
+        hf_config = self.speculative_config.draft_model_config.hf_config
+        if not hasattr(hf_config, "eagle_aux_hidden_state_layer_ids"):
+            return None
+
+        layer_ids = hf_config.eagle_aux_hidden_state_layer_ids
+        if layer_ids and isinstance(layer_ids, (list, tuple)):
+            return tuple(layer_ids)
+
+        return None
+
     def reload_weights(self) -> None:
         assert getattr(self, "model", None) is not None, (
             "Cannot reload weights before model is loaded."